Python pad示例

示例#1

def _transit(network, n_filters):
  left = _normalized_convolution(X=network, n_filters=n_filters, kernel_shape=(3, 3), stride=(2, 2), pad=(1, 1))
  left = _normalized_convolution(X=left, n_filters=n_filters, kernel_shape=(3, 3), stride=(2, 2), pad=(1, 1))
  right = layers.pooling(X=network, mode='average', kernel_shape=(2, 2), stride=(2, 2), pad=(0, 0))
  pad_width = (0, 0, 0, n_filters / 2, 0, 0, 0, 0)
  right = layers.pad(right, pad_width, 'constant')
  return right + right

示例#2

def _transit(network, module_index):
  n_filters = {0 : 16, 1 : 32, 2 : 64}[module_index]
  if module_index == 0:
    network = _normalized_weighted_convolution(network, (3, 3), n_filters, (2, 2), (1, 1))
    return network
  else:
    P = _normalized_weighted_convolution(network, (3, 3), n_filters, (2, 2), (1, 1))
    P = _normalized_weighted_convolution(P, (3, 3), n_filters, (1, 1), (1, 1))
    Q = layers.pooling(X=network, mode='average', kernel_shape=(2, 2), stride=(2, 2), pad=(0, 0))
    Q = layers.pad(Q, (0, 0) + (0, n_filters / 2) + (0, 0) * 2, 'constant')
    return P + Q

示例#3

def _transit(network, n_filters):
  network = layers.pooling(X=network, mode='average', kernel_shape=(2, 2), stride=(2, 2), pad=(0, 0))
  network = layers.batch_normalization(network)
  pad_width = (0, 0, 0, n_filters / 2, 0, 0, 0, 0)
  network = layers.pad(network, pad_width, 'constant')
  return network